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Aerial Photo Image Retrieval Using Adaptive Image Classification

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

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Abstract

The paper presents a method for content based image retrieval (CBIR) using an adaptive image classification with Radial Basis Function networks. It supports geographical image retrieval over digitized historical aerial photographs, in a digital library, which are gray-scaled and low-resolution images. CBIR is achieved on the basis of texture feature extraction and image classification. Feature extraction methods for geographical image analysis are Gabor spectral filtering and Laws’ energy filtering, which are the most widely used in image classification and segmentation. Image classification supports effective CBIR through composite classifier models dealing with multi-modal feature distribution. The method is evaluated over a digital library that contains collections of thousands of small-sized texture tiles obtained from large-sized aerial photograph images with geographical features.

This study was supported by a grant of the Seoul R&BD Program.

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© 2006 Springer-Verlag Berlin Heidelberg

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Baik, S.W., Jeong, M.S., Baik, R. (2006). Aerial Photo Image Retrieval Using Adaptive Image Classification. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_37

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  • DOI: https://doi.org/10.1007/11893011_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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